Top Senior Data Science Jobs

Lead data science strategy for newsroom audience analytics by building predictive and explanatory models, metrics, forecasts, and measurement systems. Partner with editorial, audience, product, and digital strategy teams to guide content and engagement decisions. Apply causal inference, experimentation, and statistical methods; translate findings into recommendations; maintain production analytics workflows; and mentor junior data scientists while advancing technical standards.

Develops statistical and machine learning models, analyzes complex datasets, builds trustworthy data assets, and generates insights that support business growth and customer behavior analysis. The role collaborates with stakeholders, data engineers, and architects; leads high-impact projects; informs experimental design; optimizes model performance; and mentors colleagues in data science and machine learning.

Lead experimentation, causal inference, advanced analytics, machine learning, and Generative AI initiatives. Design and analyze marketing and product experiments, build scalable predictive and prescriptive models, establish statistical governance, develop data pipelines, influence executive stakeholders, and mentor data scientists. Translate complex business challenges into measurable analytical solutions across marketing, product, sales, risk, and compliance functions.

Build actuarial and health-economic models (total cost of care, PMPM, MLR) from claims to project multi-year cost and utilization impacts, quantify uncertainty, support commercial pricing, defend analyses to payer actuaries, partner on causal/expt design, and produce outputs usable for pricing/reserving.

Conduct data science, engineering, and analytics work supporting Department of War and Intelligence Community clients. Test and evaluate AI/ML models, potentially work with large language models, perform all-source analysis, and collaborate with multidisciplinary teams. The role requires an active TS/SCI clearance with a recent CI polygraph and a bachelor’s degree or equivalent direct experience.

Leads and mentors junior developers while designing, modifying, testing, and deploying data solutions in Palantir Foundry. Builds data models and reports from ERP and legacy systems, translates business requirements into technical capabilities, and supports IT modernization. Requires Python, PySpark, cloud architecture, CI/CD, database design, Army domain knowledge, and an active DoD Secret clearance.

Drive data-informed decisions across Product, Sales, and Engineering by analyzing usage patterns, business performance, and trends. Build data pipelines, write SQL and Python analyses, create Looker and Data Studio dashboards, monitor opportunities and risks, and enable teams to use analytics tools effectively.

The role involves analyzing fraud and risk data, defining KPIs, collaborating on anti-fraud strategies, and creating tools and dashboards for data accessibility at Whatnot.

Develop and productionize AI and machine learning solutions for client, investment, and operational problems. Responsibilities include designing AI agents and agentic workflows, prototyping with foundation models and agent frameworks, building evaluation and observability systems, ensuring reliability and compliance, translating business needs into technical solutions, leading major initiatives, and presenting research and technical work to senior stakeholders.

Staff and Principal Product Data Scientists will use advanced analytics, experimentation, causal inference, machine learning, and large-scale data technologies to guide Slack product strategy. They will define success metrics, build self-service reporting and data models, evaluate product adoption drivers, identify growth opportunities, and help develop Slack’s agentic data delivery platform. The role partners closely with Product, Design, Engineering, Product Research, and go-to-market teams while communicating insights to executives and cross-functional stakeholders.

Analyzes structured and unstructured financial and ERP data using Python, SAS, statistical methods, predictive modeling, and advanced algorithms. Supports Oracle E-Business Suite financial processes, data conversion, reporting, and continuity of operations planning. Develops models and actionable insights for business decisions, collaborates with engineering and disaster recovery teams, maintains contingency plans, and executes recovery exercises. Requires a master’s degree, 5+ years of experience, Oracle expertise, certification eligibility, and approximately 25% annual travel to Washington, DC.

Build and productionize data pipelines and LLM-based, multimodal AI solutions for insurance use cases. Develop agentic AI applications using RAG, embeddings, prompting, custom tools, and evaluation frameworks. Apply responsible-AI practices, deploy and monitor machine learning systems, and lead client workshops to translate business needs into technical solutions and roadmap priorities.

Consult as a fractional data scientist for hedge funds, private equity firms, and family offices. Lead data science initiatives: build infrastructure, develop and deploy models, create portfolio dashboards, monitor post-deployment performance, and partner with investment teams to turn data into actionable investment insights. Advise on vendor selection, data quality, technology stacks, KPIs, and governance.

Develop and deploy AI and data-driven systems for reactor servicing, focusing on RAG, LLMs, NLP, safety documentation, and operational procedures. Build Plotly Dash interfaces, optimize Python workflows, package and containerize applications, and collaborate with engineering and IT teams on reliable, compliant AI solutions. The role requires an active DOE L or DoD Secret clearance and supports up to 10% telework.

Develop and evaluate machine learning and statistical models supporting intelligence analysis. Curate, transform, and integrate data; build classifiers and anomaly detection systems; deploy and refine models in production; monitor model performance and data accuracy; collaborate with analysts and software developers; document processes and datasets; and communicate findings to stakeholders.

Build end-to-end data science, machine learning, forecasting, time-series, and AI solutions for supply chain and manufacturing operations. Develop analytical datasets, ETL/ELT workflows, dashboards, APIs, and production models using Python, SQL, cloud platforms, and modern data technologies. Partner with business and technical stakeholders to improve inventory, demand forecasting, delivery, production planning, cost, and operational efficiency. The role owns solutions from problem definition through deployment, monitoring, adoption, and continuous improvement.

Develops and manages predictive marketing models, including propensity, cross-sell, upsell, churn, and attribution models. Oversees data sources, model validation, recalibration, MLOps governance, monitoring, and lifecycle management. Delivers marketing analytics projects, improves workflows, applies emerging AI tools, and communicates data-driven recommendations to nontechnical stakeholders. Requires strong statistical modeling, programming, SQL, visualization, cloud data, and cross-functional decision-making skills.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Develop statistical and machine learning models for clinical trial enrollment, patient matching, forecasting, and trial management. Monitor model performance and bias, build scalable data pipelines using AWS services and dbt, and ensure compliance with HIPAA, GDPR, and privacy standards. Collaborate with ML engineering, product, and clinical research stakeholders to integrate models into practical healthcare solutions.

Drive product-led growth through rigorous analysis of onboarding, activation, adoption, retention, expansion, and monetization. Partner with Product, Engineering, Design, Marketing, and Sales to design experiments, analyze funnels and cohorts, identify behavioral signals, build metrics and dashboards, improve instrumentation, and translate uncertain or incomplete data into actionable product recommendations.

Develop and deploy AI, machine learning, statistical, and adaptive process-control models for bioprocess optimization. Analyze sensor, batch, and experimental data; improve fermentation and downstream processing productivity, yield, cost, and scalability. Collaborate with engineers and scientists to integrate data science solutions into manufacturing and R&D operations, enhance data pipelines and modeling frameworks, and communicate findings to technical and non-technical stakeholders.

Lead growth analytics across acquisition, activation, approval, signup, and retention. Partner with product, design, engineering, and marketing leaders to define metrics, size opportunities, prioritize roadmaps, analyze experiments, and deliver strategic recommendations. Build funnel, cohort, and retention analyses; collaborate on self-service metrics; and communicate evidence-based findings to technical teams and executives.

Leads predictive modeling and machine learning initiatives to solve business problems and improve member experiences. Provides methodology guidance, coaches junior data scientists, develops and optimizes models, processes massive structured and unstructured datasets, manages batch and real-time scoring, and communicates findings to business partners. The role requires advanced statistical modeling, distributed computing, cloud analytics, deep learning, data engineering, and healthcare data analysis involving medical records, claims, and eligibility data.

Designs, deploys, and improves production machine learning and statistical models for identity resolution, entity matching, measurement, and analytics. Responsibilities include transforming large datasets, engineering features, developing evaluation and experimentation frameworks, monitoring deployed systems, communicating recommendations, partnering with product and engineering, and mentoring data science colleagues.

Build end-to-end data engineering and data science solutions for robotics and manufacturing data. Responsibilities include designing scalable multimodal data pipelines, creating analytic datasets, applying statistical and machine learning methods, developing MLOps workflows, building APIs and internal tools, and improving data governance, reliability, automation, and production support. The role partners cross-functionally with robotics, manufacturing, software, and data teams.